The Effects of Market Competition on Cardiologists’ Adoption of Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
BACKGROUND: For decades, the prevailing assumption regarding the diffusion of high-cost medical technologies has been that competitive markets favor more aggressive adoption of new treatments by health care providers (ie, the "Medical Arms Race"). However, novel regulations governing the adoption of transcatheter aortic valve replacement (TAVR) may have disrupted this paradigm when TAVR was introduced. OBJECTIVE: The objective of this study was to assess the relationship between the market concentration of physician group practices and the adoption of TAVR in its first years of use. RESEARCH DESIGN: This was a retrospective cohort study. SUBJECTS: Physician group practices (n=5116) providing interventional cardiology services in the United States from May 1, 2012, to December 31, 2014. MEASURES: The first use of TAVR as indicated by a fee-for-service Medicare claim. Covariates including characteristics of the physician groups (ie, case volume, hospital affiliation, mean patient risk) as well as county-level and market-level characteristics. RESULTS: By the close of 2014, 9.3% of practices had adopted TAVR. Cox proportional hazards models revealed a hazard ratio of 1.26 (95% confidence interval: 1.16-1.37, P<0.001) per 1000 point increase in the physician group practice Herfindahl-Hirschman Index, indicating each 1000 point increase in group practice Herfindahl-Hirschman Index was associated with a 26% relative increase in the rate of TAVR adoption. CONCLUSIONS: Adoption of TAVR by physician groups in concentrated markets was potentially a consequence of the unique regulations governing TAVR reimbursement, which favored the adoption of TAVR by physician groups with greater market power. These findings have important implications for how future regulations may shape patterns of technology adoption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".